def addShape(shapefilename, lw=0.0):
r = shapefile.Reader(shapefilename)
shapes = r.shapes()
records = r.records()
cnt = 0
for record, shape in zip(records, shapes):
print(cnt)
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,), zorder=3)
lines.set_facecolors(np.random.rand(3, 1) * 0.5 + 0.5)
lines.set_edgecolors('k')
lines.set_linewidth(lw)
ax.add_collection(lines)
cnt += 1
def add_data(globe, axes, color_dict):
"""Add shapefile polygons to the matplotlib axes"""
file_object = shapefile.Reader(filename)
shapes = file_object.shapes()
records = file_object.records()
#iterate over all but the first 20 polygons (they're junk)
for record, shape in zip(records[20:],shapes[20:]):
#this entry is the colour code
description = record[6]
lons,lats = zip(*shape.points)
#transform the lat/long coords to the right projection
data = np.array(globe(lons, lats)).T
#shapefile shapes can have disconnected parts, we have
#to check
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
#add all the parts
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
#Add all the parts we've found as a set of lines
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_facecolors(color_dict[description])
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
#add the collection to the active axes
axes.add_collection(lines)
def plot_view(result):
# Determine bounding box if no clipping boundary was supplied
if not result['bbox']:
result['bbox'] = bbox_of_view(result)
ax = plt.subplot(111)
# plt.box(on=None)
m = Basemap(resolution='i',
projection='merc',
llcrnrlat=result['bbox']['ymin'],
urcrnrlat=result['bbox']['ymax'],
llcrnrlon=result['bbox']['xmin'],
urcrnrlon=result['bbox']['xmax'],
lat_ts=(result['bbox']['xmin'] +
result['bbox']['xmax']) / 2)
m.drawcoastlines()
try:
for el in result['results']:
vectors = get_vectors_from_postgis_map(m, loads(el['geom']))
lines = LineCollection(vectors, antialiaseds=(1, ))
lines.set_facecolors('black')
lines.set_edgecolors('white')
lines.set_linewidth(1)
ax.add_collection(lines)
m.fillcontinents(color='coral', lake_color='aqua')
# If AttributeError assume geom_type 'Point', simply collect all
# points and perform scatterplot
except AttributeError:
xy = m([loads(point['geom']).x for point in result['results']],
[loads(point['geom']).y for point in result['results']])
plt.scatter(xy[0], xy[1])
plt.show()
def plot(self,
ax=None,
cmapname=None,
cmap=None,
linewidth=1,
edgecolor='grey',
facecolor=None,
alpha=1):
"""Plot the geometries on the basemap using the defined colors
Parameters:
-----------
ax : matplotlib.axis object
An axis object for plots. Overwrites the self.ax attribute.
cmapname : string
Name of the color map from matplotlib (LINK!) (default: 'seismic')
cmap : matplotlib colormap
You can create you own colormap and pass it to the plot.
linewidth : float
Width of the lines.
edgecolor : string, float or iterable
Definition of the edge color. Can be an iterable with a color
definition for each geometry, a string with one color for
all geometries or a float to define one color for all geometries
from the cmap.
facecolor : string, float or iterable
Definition of the face color. See edgecolor.
alpha : float
Level of transparency.
"""
if ax is not None:
self.ax = ax
n = 0
if facecolor is None:
facecolor = self.color
if edgecolor is None:
edgecolor = self.color
if cmapname is not None:
self.cmapname = cmapname
if self.data is not None:
self.data = np.array(self.data)
if cmap is None:
cmap = plt.get_cmap(self.cmapname)
for geo in self.geometries:
vectors = self.get_vectors_from_postgis_map(geo)
lines = LineCollection(vectors, antialiaseds=(1, ))
lines.set_facecolors(self.select_color(facecolor, cmap, n))
lines.set_edgecolors(self.select_color(edgecolor, cmap, n))
lines.set_linewidth(linewidth)
lines.set_alpha(alpha)
self.ax.add_collection(lines)
n += 1
def draw_map(dpts_dataframe, topic_index):
m, ax = carte_france()
departements = 'data/DEPARTEMENT/DEPARTEMENT.shp'
shp = departements
r = shapefile.Reader(shp)
shapes = r.shapes()
records = r.records()
records = clean_records(records)
done = False
for record, shape in zip(records, shapes):
geo_points = [lambert932WGPS(x, y) for x, y in shape.points]
lons = [_[0] for _ in geo_points]
lats = [_[1] for _ in geo_points]
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [
data,
]
else:
# un polygone est une liste de sommets
# on le transforme en une liste d'arêtes
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
# pour changer les couleurs c'est ici, il faudra utiliser le champ records
# pour les changer en fonction du nom du départements
if not done:
for i, _ in enumerate(record):
print(i, _)
done = True
# dep = retourne_vainqueur(record[2])
dep = True
if dep is not None:
couleur = colors[int(dpts_dataframe['code_couleur'].loc[
dpts_dataframe[0] == int(record[1])].values[0]), :]
lines.set_facecolors(couleur)
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
else:
print("--- issue with", record[-1])
plt.savefig('heatmaps/heatmap_topic' + str(topic_index) + '.png')
def plot_countries(countries, world):
if type(countries) != list:
countries = [countries]
ax = plt.subplot(111)
m = get_region_map(countries, world)
for country in countries:
shpsegs = []
for ring in country['SHAPE']:
x, y = geo2map_coord(ring, m)
shpsegs.append(zip(x,y))
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(cm.jet(np.random.rand(1)))
lines.set_edgecolors('k')
#lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.show()
def drawShape(m, ax, paraTable):
for conshpfn in paraTable["ShapeFile"].unique():
for i, conShape in enumerate(getPointArray(conshpfn)):
tempParaTable = concat([paraTable[paraTable["ShapeFile"]==conshpfn]], ignore_index = True)
partLimit = tempParaTable.ix[i,"PartLimit"]
normal = tempParaTable.ix[i,"Normal"]
shpsegs = []
for j, conShapePart in enumerate(conShape):
if j < partLimit:
lons, lats = zip(*conShapePart)
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(cm.gray(1 - normal))
lines.set_linewidth(0.01)
ax.add_collection(lines)
def mapcountries(countries): #Map list of countries?
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(11.7, 8.3))
plt.subplots_adjust()
m = Basemap(projection='hammer', resolution=None, lon_0=0)
m.drawcountries(linewidth=0.5)
m.drawcoastlines(linewidth=0.5)
from shapelib import ShapeFile
import dbflib
from matplotlib.collections import LineCollection
from matplotlib import cm
#for ctry in countries:
shp = ShapeFile(r"borders/world_adm1")
dbf = dbflib.open(r"borders/world_adm1")
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_obj(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(
lats) < -91.:
raise ValueError('Lats/Lons out of range')
x, y = m(lons, lats)
shpsegs.append(zip(x, y))
if ring == 0:
shapedict = dbf.read_record(npoly)
name = shapedict["NAME_1"]
shapedict['RINGNUM'] = ring + 1
shapedict['SHAPENUM'] = npoly + 1
shpinfo.append(shapedict)
print(name)
lines = LineCollection(shpsegs, antialiaseds=(1, ))
lines.set_facecolors(cm.jet(0.5))
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.show()
def algo (region,output, word):
reg = [0 for i in range(0,23)]
total = 0
for line in region: #reg_num number
items = line.rstrip('\n').split('\t')
reg[int(items[0])]=int(items[1])
total = total+int(items[1])
reg=np.array(reg)
max_percent=np.max(reg)
plt.figure(figsize=(15,15))
ax = plt.subplot(111)
m = Basemap(projection='merc', lat_0=45, lon_0=0,
resolution = 'h', area_thresh = 10.0,
llcrnrlat=41.33, llcrnrlon=-5,
urcrnrlat=51.5, urcrnrlon=9.7)
m.drawcoastlines()
#m.drawcountries() # french border does not fit with region ones
m.fillcontinents(color='lightgrey',lake_color='white')
m.drawmapboundary(fill_color='white')
sf = shapefile.Reader("./geodata/FRA_adm1")
shapes = sf.shapes()
records = sf.records()
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_edgecolors('k')
lines.set_linewidth(0.5)
lines.set_facecolors('brown')
lines.set_alpha(float(reg[record[3]])/max_percent) #record[3] est le numero de region
ax.add_collection(lines)
plt.savefig(word+'-'+str(total)+'.png',dpi=300)
return 0
def plot(self, ax=None, cmapname=None, cmap=None, linewidth=1,
edgecolor='grey', facecolor=None, alpha=1):
"""Plot the geometries on the basemap using the defined colors
Parameters:
-----------
ax : matplotlib.axis object
An axis object for plots. Overwrites the self.ax attribute.
cmapname : string
Name of the color map from matplotlib (LINK!) (default: 'seismic')
cmap : matplotlib colormap
You can create you own colormap and pass it to the plot.
linewidth : float
Width of the lines.
edgecolor : string, float or iterable
Definition of the edge color. Can be an iterable with a color
definition for each geometry, a string with one color for
all geometries or a float to define one color for all geometries
from the cmap.
facecolor : string, float or iterable
Definition of the face color. See edgecolor.
alpha : float
Level of transparency.
"""
if ax is not None:
self.ax = ax
n = 0
if facecolor is None:
facecolor = self.color
if edgecolor is None:
edgecolor = self.color
if cmapname is not None:
self.cmapname = cmapname
if self.data is not None:
self.data = np.array(self.data)
if cmap is None:
cmap = plt.get_cmap(self.cmapname)
for geo in self.geometries:
vectors = self.get_vectors_from_postgis_map(geo)
lines = LineCollection(vectors, antialiaseds=(1, ))
lines.set_facecolors(self.select_color(facecolor, cmap, n))
lines.set_edgecolors(self.select_color(edgecolor, cmap, n))
lines.set_linewidth(linewidth)
lines.set_alpha(alpha)
self.ax.add_collection(lines)
n += 1
def _collect_geoms(self, query_object, ax, m, el_limit=5000):
"""
Create vectors for different geom and multi-geom types
:rtype: subplot
:param query_object:
:param ax: Instance of plot.subplot()
:param m: Instance of Basemap()
:param el_limit: Maximum number of elements to display on map
:return: subplot instance
"""
# Collect fetched geometries
if not len(query_object.results) > el_limit:
try:
for el in query_object.results:
vectors = query_object._get_vectors_from_postgis_map(m,
loads(
el[
'geom']))
lines = LineCollection(vectors, antialiaseds=(1,))
if not query_object.geom_type == 'LineString':
lines.set_facecolors('red')
lines.set_linewidth(0.25)
ax.add_collection(lines)
# If AttributeError assume geom_type 'Point', simply collect all
# points and perform scatterplot
except AttributeError:
xy = m(
[loads(point['geom']).x for point in query_object.results],
[loads(point['geom']).y for point in query_object.results])
ax.scatter(xy[0], xy[1])
# # Add clipping border
# if self.region.boundary_polygon:
# vectors = self.__get_vectors_from_postgis_map(m, loads(
# self.region.boundary_polygon))
# border = LineCollection(vectors, antialiaseds=(1,))
# border.set_edgecolors('black')
# border.set_linewidth(1)
# border.set_linestyle('dashed')
# ax.add_collection(border)
else:
logger.printmessage.error("Error: >5000 elements to plot!")
return ax
def drawShape(m, ax, paraTable):
for conshpfn in paraTable["ShapeFile"].unique():
for i, conShape in enumerate(getPointArray(conshpfn)):
tempParaTable = concat(
[paraTable[paraTable["ShapeFile"] == conshpfn]],
ignore_index=True)
partLimit = tempParaTable.ix[i, "PartLimit"]
normal = tempParaTable.ix[i, "Normal"]
shpsegs = []
for j, conShapePart in enumerate(conShape):
if j < partLimit:
lons, lats = zip(*conShapePart)
x, y = m(lons, lats)
shpsegs.append(zip(x, y))
lines = LineCollection(shpsegs, antialiaseds=(1, ))
lines.set_facecolors(cm.gray(1 - normal))
lines.set_linewidth(0.01)
ax.add_collection(lines)
def plotCountry(m, ax, id, path='gadm0'):
country, lonlat = merged[id]
r = shapefile.Reader(r"%s/%s_adm0" % (path, country))
shapes = r.shapes()
records = r.records()
for record, shape in zip(records, shapes):
lons, lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
lines.set_facecolors('lightgreen')
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
lines.set_alpha(0.5)
ax.add_collection(lines)
# Add country centroid
lon, lat = lonlat
xpt, ypt = m(lon, lat)
txt = ax.annotate(id, (xpt, ypt),
color='r',
size='medium',
ha='center',
va='center',
path_effects=[
PathEffects.withStroke(linewidth=3, foreground="w"),
PathEffects.withSimplePatchShadow()
])
def drawcountry(self,
ax,
base_map,
iso2,
color,
alpha = 1):
if iso2 not in self.countries:
raise ValueError, "Where is that country ?"
vertices = self.countries[iso2]
shape = []
for vertex in vertices:
longs, lats = zip(*vertex)
# conversion to plot coordinates
x,y = base_map(longs, lats)
shape.append(zip(x,y))
lines = LineCollection(shape,antialiaseds=(1,))
lines.set_facecolors(cm.hot(np.array([color,])))
lines.set_edgecolors('white')
lines.set_linewidth(0.5)
lines.set_alpha(alpha)
ax.add_collection(lines)
def polyPlotShapeFile(m, ax, sf, crs):
import numpy as np
import pyproj
from matplotlib.collections import LineCollection
from matplotlib import cm
import matplotlib.pyplot as plt
shapes = sf.shapes()
records = sf.records()
shp_proj = pyproj.Proj(crs.to_proj4()) #.shp file projection
std_proj = pyproj.Proj(init='epsg:4326') # Lat/Lon system
for record, shape in zip(records, shapes):
lons, lats = zip(*shape.points)
lons, lats = pyproj.transform(shp_proj, std_proj, lons, lats)
data = np.array(m(lons, lats)).T
if record['POLY_TYPE'] == 'I':
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,
antialiaseds=(1, ),
zorder=1,
alpha=0.2)
lines.set_facecolors('b')
# lines.set_facecolors(cm.jet(np.random.rand(1)))
lines.set_edgecolors('k')
lines.set_linewidth(1)
ax.add_collection(lines)
def plotclustering(label, zipcodes, nameee):
# print len(zipcodes)
label = np.array(label)
zipcodes = np.array(zipcodes)
cluster1 = zipcodes[label == 1]
cluster2 = zipcodes[label == 2]
cluster0 = zipcodes[label == 0]
cluster3 = zipcodes[label == 3]
fig = plt.figure(figsize =(15, 15))
ax = plt.subplot(111)
lllat = 40.473; urlat = 40.93; lllon = -74.27; urlon = -73.69 # define the boundary of the map
m = Basemap(ax=ax, projection = 'stere', lon_0 = (urlon + lllon)/2, lat_0 = (urlat +lllat)/2, llcrnrlon = lllon, llcrnrlat = lllat, urcrnrlon = urlon, urcrnrlat = urlat, resolution= 'l')# create the basemap
# m.drawcoastlines()
m.drawcountries()
shp = ShapeFile('c:/Users/gang/Desktop/nyczipregion')
dbf = dbflib.open('c:/Users/gang/Desktop/nyczipregion')
for npoly in range(shp.info()[0]):
shpsegs = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons , lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x, y))
if ring ==0:
shapedict = dbf.read_record(npoly)
name = shapedict['Zcta5ce00']
lines = LineCollection(shpsegs, antialiaseds=(1,))
if name in cluster0:
lines.set_facecolors('b')
if name in cluster1:
lines.set_facecolors('g')
if name in cluster2:
lines.set_facecolors('r')
if name in cluster3:
lines.set_facecolors('y')
lines.set_alpha(1)
lines.set_edgecolors('k')
ax.add_collection(lines)
plt.title('Box Clustering Based On Taxi Trips')
plt.savefig(nameee+'_box_trip_Clustering.png', dpi=300)
plt.show()
def generate_map(countries):
# Initialize plotting area, set the boundaries and add a sub-plot on which
# we are going to plot the map
fig = plt.figure(figsize=(11.7, 8.3))
plt.subplots_adjust(left=0.05,
right=0.95,
top=0.90,
bottom=0.05,
wspace=0.15,
hspace=0.05)
ax = plt.subplot(111)
# Initialize the basemap, set the resolution, projection type and the viewport
bm = Basemap(resolution='i', projection='robin', lon_0=0)
# Tell basemap how to draw the countries (built-in shapes), draw parallels and meridians
# and color in the water
bm.drawcountries(linewidth=0.5)
bm.drawparallels(np.arange(-90., 120., 30.))
bm.drawmeridians(np.arange(0., 360., 60.))
bm.drawmapboundary(fill_color='aqua')
# Open the countries shapefile and read the shape and attribute information
r = shapefile.Reader('world_borders/TM_WORLD_BORDERS-0.3.shp')
shapes = r.shapes()
records = r.records()
# Iterate through all records (attributes) and shapes (countries)
for record, shape in zip(records, shapes):
# Extract longitude and latitude values into two separate arrays then
# project the coordinates onto the map projection and transpose the array, so that
# the data variable contains (lon, lat) pairs in the list.
# Basically, the following two lines convert the initial data
# [ [lon_original_1, lat_original_1], [lon_original_2, lat_original_2], ... ]
# into projected data
# [ [lon_projected_1, lat_projected_1, [lon_projected_2, lat_projected_2], ... ]
#
# Note: Calling baseshape object with the coordinates as an argument returns the
# projection of those coordinates
lon_array, lat_array = zip(*shape.points)
data = np.array(bm(lon_array, lat_array)).T
# Next we will create groups of points by splitting the shape.points according to
# the indices provided in shape.parts
if len(shape.parts) == 1:
# If the shape has only one part, then we have only one group. Easy.
groups = [
data,
]
else:
# If we have more than one part ...
groups = []
for i in range(1, len(shape.parts)):
# We iterate through all parts, and find their start and end positions
index_start = shape.parts[i - 1]
index_end = shape.parts[i]
# Then we copy all point between two indices into their own group and append
# that group to the list
groups.append(data[index_start:index_end])
# Last group starts at the last index and finishes at the end of the points list
groups.append(data[index_end:])
# Create a collection of lines provided the group of points. Each group represents a line.
lines = LineCollection(groups, antialiaseds=(1, ))
# We then select a color from a color map (in this instance all Reds)
# The intensity of the selected color is proportional to the number of requests.
# Color map accepts values from 0 to 1, therefore we need to normalize our request count
# figures, so that the max number of requests is 1, and the rest is proportionally spread
# in the range from 0 to 1.
max_value = float(max(countries.values()))
country_name = record[4]
requests = countries.get(country_name, 0)
requests_norm = requests / max_value
lines.set_facecolors(cm.Reds(requests_norm))
# Finally we set the border color to be black and add the shape to the sub-plot
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
# Once we are ready, we save the resulting picture
plt.savefig('requests_per_country.png', dpi=300)
index = shape.parts[i-1] index2 = shape.parts[i] segs.append(data[index:index2]) segs.append(data[index2:]) lines = LineCollection(segs,antialiaseds=(1,)) #Now obtain the data in a given poly and assign a color to the value div = str(record[5]) dval = divlookup(dfile,div,yyyy,int(mm)) if(len(div) < 4): div = '0'+div nval = normlookup(normfile, div, int(mm)) dval = dval-nval #Now normalize the data and map it to the color ramp dval = (dval - valmin) * (255/(valmax-valmin)) #if(dval > valmax): dval = valmax - 0.1 lines.set_facecolors(cmap(int(dval))) lines.set_edgecolors(cmap(int(dval))) lines.set_linewidth(0.25) ax1.add_collection(lines) if(imgsize == 'GEO'): inProj = Proj(init='epsg:3338') outProj = Proj(init='epsg:4326') #Now read in the Alaska Climate Division Shapes and fill the basemap s = shapefile.Reader(r"./Shapefiles/AK_divisions_NAD83") shapes = s.shapes() records = s.records() for record, shape in zip(records,shapes): lons,lats = zip(*shape.points)
def drawMap(filename,names,totalCount):
countries = json.loads(open(filename).read())["features"]
for country in countries:
name = country["properties"]["name"]
polygonList = country["geometry"]["coordinates"]
polygonType = country["geometry"]["type"]
shpsegs = []
print "Processing %s [%d/%s] ..." % (unicode(name).encode("utf-8"), len(polygonList), polygonType)
maxverts = 0
maxindex = 0
counter = 0
for polygon in polygonList:
if polygonType == "MultiPolygon":
for p in polygon:
if len(p) == 1:
p = p[0]
if p[0] != p[-1]:
p.append(p[0])
lons, lats = zip(*p)
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if len(x) > maxverts:
maxverts = len(x)
maxindex = counter
counter+=1
else:
if len(polygon) == 1:
polygon = polygon[0]
if polygon[0] != polygon[-1]:
polygon.append(polygon[0])
lons, lats = zip(*polygon)
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if len(x) > maxverts:
maxverts = len(x)
maxindex = counter
counter+=1
# Compute centroid
centroid = Polygon(shpsegs[maxindex]).centroid
# Create country shape
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_edgecolors('k')
lines.set_linewidth(0.5)
#import brewer2mpl
#colormap = brewer2mpl.get_map('Paired', 'qualitative', 12).mpl_colors
colormap = [(0.6509803921568628, 0.807843137254902, 0.8901960784313725), (0.12156862745098039, 0.47058823529411764, 0.7058823529411765), (0.6980392156862745, 0.8745098039215686, 0.5411764705882353), (0.2, 0.6274509803921569, 0.17254901960784313), (0.984313725490196, 0.6039215686274509, 0.6), (0.8901960784313725, 0.10196078431372549, 0.10980392156862745), (0.9921568627450981, 0.7490196078431373, 0.43529411764705883), (1.0, 0.4980392156862745, 0.0), (0.792156862745098, 0.6980392156862745, 0.8392156862745098), (0.41568627450980394, 0.23921568627450981, 0.6039215686274509), (1.0, 1.0, 0.6), (0.6941176470588235, 0.34901960784313724, 0.1568627450980392)]
# Add color and label if covered by Safecast
if name in names.keys():
color = colormap[(int((float(names[name][0])/totalCount)*12)+1)]
lines.set_label("%s - %0.1fK (%d)" % (name, names[name][0]/1000.0, names[name][1]) )
#lines.set_label(name)
lines.set_edgecolors(color)
lines.set_facecolors(color)
label = plt.text(centroid.x, centroid.y, "%d" % names[name][1], fontsize=5, ha='center', va='center', color='k', fontweight='bold')
plt.setp(label, path_effects=[PathEffects.withStroke(linewidth=2, foreground="w")])
ax.add_collection(lines)
def choropleth(self, adm_num_dicts, level = 'department', source='gadm', cmap_base=plt.cm.YlOrRd, ret_colormap_and_label=True, bin_lims=None, nbins=5, linewidth=0.4):
"""
A choropleth map by department or municipality. The dataset for boundaries of administrative areas is taken from GADM database
Original code is from here: https://github.com/astivim/Nicaragua-Population-Density-Map
Input:
- adm_num_dicts: a list of dictionaries with the following key-value pairs: {'adm' : str, 'num' : float}
- bin_lims (optional): values of bin edges
- nbins (optional): number of bins, default = 5. Ignored if bin_lims is specified.
- cmap_base: base colormap. Default is plt.cm.YlOrRd (red)
- ret_colormap_and_label=True. If True, return colormap_label = {'bin_labels' : bin_labels, 'colormap' : custom_cmap} used for plotting. If False, colormap_label = None
Return: colormap_label = {'bin_labels' : bin_labels, 'colormap' : custom_cmap}
"""
# Divide nums into bins. For each entry in nums, assign to which bin it belongs to, then assign a color to each bin.
adms = [item['adm'] for item in adm_num_dicts]
nums = [item['num'] for item in adm_num_dicts]
# Define bin_lims by dividing the range of nums into equally spaced portions
if bin_lims:
nbins = len(bin_lims)
else:
temp_nums = [num for num in nums if num]
bin_spacing = (max(temp_nums) - min(temp_nums) + 1) / nbins
bin_lims = np.arange(min(temp_nums), max(temp_nums) + bin_spacing, bin_spacing)
nbins = len(bin_lims)
# Assign each num in nums into its respective bin and assign color. Use only nbins values from the colormap (cmap)
nums_bin_ix = np.digitize(nums, bin_lims)
nums_bin_ix = [idx if idx < nbins else 0 for idx in nums_bin_ix]
cmaplist = [cmap_base(i) for i in range(cmap_base.N)]
del_colors = np.int(np.ceil(cmap_base.N/float(nbins-1)))
map_colors = cmaplist[0::del_colors]
map_colors.insert(0, (1.0, 1.0, 1.0, 1.0)) # fill with white if no data (num is None)
# Load the geographical data (shapefile and record)
if level == 'department':
fpath = os.path.join(self.data_dir, "NIC_adm/NIC_adm1")
elif level == 'municipality':
fpath = os.path.join(self.data_dir, "NIC_adm/NIC_adm2")
else:
print("Level unrecognized. Use 'department'")
fpath = os.path.join(self.data_dir, "NIC_adm/NIC_adm1")
r = shapefile.Reader(fpath)
shapes = r.shapes()
records = r.records()
# Extract lat and lon data from the GADM shapefile and assign a facecolor to each department
for record, shape in zip(records, shapes):
lons, lats = zip(*shape.points)
data = [(lon, lat) for lon, lat in zip(*self(lons, lats))]
if level == 'department':
adm_name = record[4]
else:
adm_name = record[6]
try:
adm_idx = adms.index(adm_name)
adm_num = adm_num_dicts[adm_idx]['num']
color_idx = nums_bin_ix[adm_idx]
except ValueError:
adm_num = None
color_idx = 0
if len(shape.parts) == 1:
segs = [data, ]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1,))
color = map_colors[color_idx] if adm_name not in ["Lago Nicaragua", "Lago de Nicaragua"] else 'aqua'
lines.set_facecolors(color)
lines.set_linewidth(linewidth)
self.ax.add_collection(lines)
if ret_colormap_and_label:
custom_cmap = mpl.colors.ListedColormap(map_colors[1: ], name='from_list')
bins = [(i1,i2) for i1,i2 in zip(bin_lims[0:nbins], bin_lims[1:nbins+1])]
bin_labels = ["(%d - %d)" % (b[0],b[1]) for b in bins]
colormap_label = {'bin_labels' : bin_labels, 'colormap' : custom_cmap}
else:
colormap_label = None
return colormap_label
shapes = shpf.shapes()
records = shpf.records()
# show only CA and AK (for example)
for record, shape in zip_filter_by_state(records, shapes, ['06', '02']):
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_facecolors(cm.jet(np.random.rand(1)))
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
plt.savefig('tutorial10.png',dpi=300)
plt.show()
# <codecell>
cmap = plt.cm.spring
for feature in neighborhoods["features"]:
if feature["properties"]["neighborhood"].upper() in list(coop_hood_avgs.neighborhood):
hood = feature["properties"]["neighborhood"].upper()
value = coop_hood_avgs_trans[hood]["avg_gross"]
color = cmap((value - vmin)/(vmax - vmin))[:3]
else:
color = "#666666"
some_points = feature["geometry"]["coordinates"]
lons, lats = np.array(some_points).T
data = np.array(m(lons, lats)).T
lines = LineCollection(data)
lines.set_edgecolors("#333333")
lines.set_facecolors(color)
lines.set_linewidth(0.3)
ax.add_collection(lines)
ax1 = fig.add_axes([0.83, 0.05, 0.03, 0.92])
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
cbar = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation="vertical")
cbar.set_label("Avg. Gross Co-op Rental Income per Sq. Ft ($)")
dv = 5.
cbar.set_ticks(np.arange(vmin, vmax+dv, dv))
cbar.ax.minorticks_on()
plt.savefig("coop_rental_map.png")
plt.show()
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
lines = LineCollection(shpsegs,antialiaseds=(1,))
if subpref_id <> 60:
colorVal = scalarMap.to_rgba(values[region_pole[subpref_region[subpref_id]]])
else:
colorVal = 'y'
print name, subpref_id, colorVal
lines.set_facecolors(colorVal)
lines.set_edgecolors('gray')
lines.set_linewidth(0.1)
ax.add_collection(lines)
# data to plot on the map
lons = []
lats = []
num = []
# if wanna plot subpref ids only
for subpref in range(1,256):
lons.append(subpref_coord[subpref][0])
lats.append(subpref_coord[subpref][1])
num.append(subpref)
def make_country_map(countrylist,
shapefilename,
m,
figname,
year=None,
bounds=(1, 2, 4, 6, 8, 10, 15, 20),
cmap=cm.YlGnBu,
logofile="../logos/logo_colloquium.png"):
"""
:param countrylist: list of country iso-codes
:type countrylist: list
:param shapefilename: path of the shape file
:type shapefilename: str
:param m: projection created with Basemap
:param figname: path of the figure to be created
:type figname: str
:param year: considered year
:type year: int
:param bounds: bounds of the colorbar
:param cmap: colormap
:param logofile: file storing the CLQ logo
:type logofile: str
:return:
"""
# Count the occurrence of each country in the list
countrycount = Counter(countrylist)
nmax = max(countrycount.values())
shapes, records = read_shape(shapefilename)
fig = plt.figure(figsize=(11.7, 8.3))
ax = plt.subplot(111)
rcParams.update({'font.size': 16})
if year is not None:
titletext = "{0}: {1} participants".format(year, len(countrylist))
plt.title(titletext, fontsize=20)
m.drawcountries(linewidth=0.1)
m.drawcoastlines(linewidth=0.1)
"""
axins = zoomed_inset_axes(ax, 2.5, loc=3)
m.drawcountries(linewidth=.2, ax=axins)
x, y = m(-15., 35.)
x2, y2 = m(27, 60.)
axins.set_xlim(x, x2)
axins.set_ylim(y, y2)
axins.axis('off')
"""
for record, shape in zip(records, shapes):
countryname = record[1]
if countryname in countrycount:
lons, lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
lines.set_facecolors(cmap(countrycount[countryname] / nmax))
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
"""
lines2 = LineCollection(segs, antialiaseds=(1,))
lines2.set_facecolors(cmap(countrycount[countryname] / nmax))
lines2.set_edgecolors('k')
lines2.set_linewidth(0.1)
axins.add_collection(lines2)
"""
cax = fig.add_axes([0.475, 0.21, 0.4, 0.02])
cmap.set_over((0., 0., 0.))
cb1 = colorbar.ColorbarBase(cax,
cmap=cmap,
norm=colors.BoundaryNorm(bounds, cmap.N),
orientation='horizontal',
spacing='uniform',
extend='max',
label='Number of participants')
cb1.ax.xaxis.set_label_position('top')
if os.path.exists(logofile):
im = plt.imread(logofile)
newax = fig.add_axes([0.85, 0.85, 0.07, 0.07], anchor='NE')
newax.imshow(im)
newax.axis('off')
# Remove frame
ax.axis('off')
# Add year in upper-left corner
# plt.annotate(year, xy=(0.05, 8.5), xycoords='axes fraction', fontsize=30)
plt.savefig(figname, dpi=300, bbox_inches='tight')
# plt.show()
plt.close()
def map_wake_hr(thersholdX):
mpl.rcParams['font.size'] = 4.4
###########################################################################################
fig = plt.figure(3)
#Custom adjust of the subplots
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
subpref_wake_hr = read_in_subpref_wake_hr(thersholdX)
# read the shapefile archive
s = m.readshapefile('/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE', 'subpref')
max7s = 1
min7s = 10000000
for subpref in subpref_wake_hr.keys():
if subpref_wake_hr[subpref] > max7s:
max7s = subpref_wake_hr[subpref]
if subpref_wake_hr[subpref] < min7s:
min7s = subpref_wake_hr[subpref]
max7s = float(max7s)
print "max", (max7s)
print "min", (min7s)
# scaled_weight = defaultdict(int)
# for i in range(256):
# scaled_weight[i] = defaultdict(int)
# for subpref in subpref_wake_hr.keys():
# scaled_weight[subpref] = (subpref_wake_hr[subpref] - min7s) / (max7s - min7s)
# values = range(256)
# jet = cm = plt.get_cmap('jet')
# cNorm = colors.Normalize(vmin=0, vmax=values[-1])
# scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
# define custom colormap, white -> nicered, #E6072A = RGB(0.9,0.03,0.16)
cdict = {'red': ( (0.0, 1.0, 1.0),
(1.0, 0.9, 1.0) ),
'green':( (0.0, 1.0, 1.0),
(1.0, 0.03, 0.0) ),
'blue': ( (0.0, 1.0, 1.0),
(1.0, 0.16, 0.0) ) }
custom_map = LinearSegmentedColormap('custom_map', cdict, N=33)
plt.register_cmap(cmap=custom_map)
subpref_coord = read_in_subpref_lonlat()
shp = ShapeFile(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
# if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(lats) < -91:
# raise ValueError,msg
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
print name, subpref_id
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
colorVal = custom_map(subpref_wake_hr[subpref_id])
# colorVal = scaled_weight[subpef]
lines.set_facecolors(colorVal)
lines.set_edgecolors('gray')
lines.set_linewidth(0.1)
ax.add_collection(lines)
# data to plot on the map
lons = []
lats = []
num = []
for subpref in subpref_wake_hr.iterkeys():
print(subpref)
if subpref <> 0 and subpref <> -1:
lons.append(subpref_coord[subpref][0])
lats.append(subpref_coord[subpref][1])
num.append(subpref_wake_hr[subpref])
x, y = m(lons, lats)
m.scatter(x, y, color='white')
for name, xc, yc in zip(num, x, y):
# draw the pref name in a yellow (shaded) box
plt.text(xc, yc, name)
plt.savefig("/home/sscepano/Project7s/D4D/CI/call_wakeup_sleep_hour/subpref_wake_" + str(thersholdX) + "pct.png",dpi=350)
def map_communities_and_commutes(G):
import networkx as nx
G_mod = nx.read_gml("/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/total_commuting_G_10com_775_269_v2.gml")
col = [str]*256
for i in range(256):
col[i] = 'w'
for node in G_mod.nodes_iter(data=True):
#print node[1]['label']
col_gephi = node[1]['graphics']['fill']
while (len(col_gephi) < 7):
col_gephi += '0'
col[int(float(node[1]['label']))] = col_gephi
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib as mpl
mpl.rcParams['font.size'] = 10.
mpl.rcParams['axes.labelsize'] = 8.
mpl.rcParams['xtick.labelsize'] = 6.
mpl.rcParams['ytick.labelsize'] = 6.
from shapelib import ShapeFile
import dbflib
from matplotlib.collections import LineCollection
from matplotlib import cm
###########################################################################################
fig = plt.figure(3)
#Custom adjust of the subplots
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
m.drawcoastlines()
shp = ShapeFile(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
# if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(lats) < -91:
# raise ValueError,msg
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(col[subpref_id])
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
from collections import defaultdict
if G.has_node(-1):
G.remove_node(-1)
max7s = 1
min7s = 1
for u,v,d in G.edges(data=True):
if d['weight'] > max7s:
max7s = d['weight']
if d['weight'] < min7s:
min7s = d['weight']
max7s = float(max7s)
print max7s
print min7s
scaled_weight = defaultdict(int)
for i in range(256):
scaled_weight[i] = defaultdict(int)
for u,v,d in G.edges(data=True):
scaled_weight[u][v] = (d['weight'] - min7s) / (max7s - min7s)
for u, v, d in G.edges(data=True):
lo = []
la = []
lo.append(lons[u])
lo.append(lons[v])
la.append(lats[u])
la.append(lats[v])
x, y = m(lo, la)
linewidth7s = scaled_weight[u][v] * 6.5 + 0.15
m.plot(x,y, linewidth= linewidth7s)
if linewidth7s > 1:
print linewidth7s
plt.savefig('/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/maps/mod_classes_10com_775_269_v2.png',dpi=1000)
#plt.show()
###################################################################################################3
return
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
num = ["%.2f" % rg[subpref_id]]
# for name, xc, yc in zip(num, x, y):
# plt.text(xc, yc, name)
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(str(1 - rg[subpref_id]))
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.savefig('/home/sscepano/D4D res/allstuff/rg/grayscale_rg_map.png',dpi=1000)
#plt.show()
def country(countries, bmap, fc=None, ec='none', lw=1, alpha=1, adm=0, gadmpath='/home/dtr/Documents/Webpages/blog-notebooks/data/TravelMap/'):
"""Colour <countries> with a <bmap> projection.
This script is adapted from:
http://www.geophysique.be/2013/02/12/
matplotlib-basemap-tutorial-10-shapefiles-unleached-continued
I downloaded the countries shapefile from the *Global Administrative Areas*
website, [gadm.org](http://gadm.org).
=> You have to use the same abbreviations for the countries as GADM does, or adjust the script.
=> You have to download the shapefiles from GADM, and extract them into the <gadmpath> directory.
Of course, you can use any other shapfiles you have, and adjust the script accordingly.
Parameters
----------
countries : string or list of strings
Countries to be plotted.
bmap : handle
As you get from bmap = Basemap().
fc : None or colour, or list of colours; <None>
Face-colour for country; if <None>, it will cycle through colour-cycle.
ec : 'none' or colour (scalar or list); <'none'>
Edge-colour for country.
lw : scalar or list; <1>
Linewidth for country.
alpha: scalar or list; <1>
Transparency.
adm : {0, 1, 2, 3}; <0>
Administrative area to choose.
gadmpath : 'string'
Absolut or relative path to shapefiles.
"""
# Ensure countries is a list
if not isinstance(countries, list):
countries = [countries,]
# Get current axis
cax = plt.gca()
# Loop through the countries
for country in countries:
# Get shapefile for the country; extract shapes and records
r = shapefile.Reader(gadmpath+country+'_adm/'+country+'_adm'+str(adm))
shapes = r.shapes()
records = r.records()
# Loop through the records; for adm0 this is only 1 run
n = 0
for record, shape in zip(records,shapes):
lons,lats = zip(*shape.points)
data = np.array(bmap(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
# If facecolor is provided, use; else cycle through colours
if fc:
if not isinstance(fc, list):
lines.set_facecolors(fc)
else:
lines.set_facecolors(fc[n])
else:
cycle = cax._get_lines.prop_cycler
lines.set_facecolors(next(cycle)['color'])
# Edge colour
if not isinstance(ec, list):
lines.set_edgecolors(ec)
else:
lines.set_edgecolors(ec[n])
# Alpha
if not isinstance(alpha, list):
lines.set_alpha(alpha)
else:
lines.set_alpha(alpha[n])
# Line width
if not isinstance(lw, list):
lines.set_linewidth(lw)
else:
lines.set_linewidth(lw[n])
# Add to current plot
cax.add_collection(lines)
n += 1
def map_num_users():
mpl.rcParams['font.size'] = 4.4
###########################################################################################
fig = plt.figure(3)
#Custom adjust of the subplots
plt.subplots_adjust(left=0.05,
right=0.95,
top=0.90,
bottom=0.05,
wspace=0.15,
hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
subpref_users = read_in_subpref_num_users()
# read the shapefile archive
s = m.readshapefile(
'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE',
'subpref')
max7s = 1
min7s = 10000000
for subpref in subpref_users.keys():
if subpref_users[subpref] > max7s:
max7s = subpref_users[subpref]
if subpref_users[subpref] < min7s:
min7s = subpref_users[subpref]
max7s = float(max7s)
print "max", (max7s)
print "min", (min7s)
scaled_weight = defaultdict(int)
for i in range(256):
scaled_weight[i] = defaultdict(int)
for subpref in subpref_users.keys():
scaled_weight[subpref] = (subpref_users[subpref] - min7s) / (max7s -
min7s)
# values = range(256)
# jet = cm = plt.get_cmap('jet')
# cNorm = colors.Normalize(vmin=0, vmax=values[-1])
# scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=jet)
# define custom colormap, white -> nicered, #E6072A = RGB(0.9,0.03,0.16)
cdict = {
'red': ((0.0, 1.0, 1.0), (1.0, 0.9, 1.0)),
'green': ((0.0, 1.0, 1.0), (1.0, 0.03, 0.0)),
'blue': ((0.0, 1.0, 1.0), (1.0, 0.16, 0.0))
}
custom_map = LinearSegmentedColormap('custom_map', cdict, N=10000)
plt.register_cmap(cmap=custom_map)
subpref_coord = read_in_subpref_lonlat()
shp = ShapeFile(
r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(
r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
#print shp_object
for ring in range(rings):
print ring
lons, lats = zip(*verts[ring])
# if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(lats) < -91:
# raise ValueError,msg
x, y = m(lons, lats)
shpsegs.append(zip(x, y))
if ring == 0:
shapedict = dbf.read_record(npoly)
print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
# print name, subpref_id
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring + 1
shapedict['SHAPENUM'] = npoly + 1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs, antialiaseds=(1, ))
colorVal = custom_map(subpref_users[subpref_id])
# colorVal = scaled_weight[subpef]
lines.set_facecolors(colorVal)
lines.set_edgecolors('gray')
lines.set_linewidth(0.1)
ax.add_collection(lines)
def plot_gspan_res(G, subpref_id, color_val):
fig = plt.figure(subpref_id)
#Custom adjust of the subplots
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
# read the shapefile archive
s = m.readshapefile('/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE', 'subpref')
m.drawcoastlines()
shp = ShapeFile(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
msg = "Out of bounds"
color_col = []
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id2 = shapedict["ID_SP"]
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
lines = LineCollection(shpsegs,antialiaseds=(1,))
if subpref_id == subpref_id2:
lines.set_facecolors('g')
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
# data to plot on the map
lons = [int]*256
lats = [int]*256
num = []
# read in coordinates fo subprefs
file_name2 = "/home/sscepano/DATA SET7S/D4D/SUBPREF_POS_LONLAT.TSV"
f2 = open(file_name2, 'r')
# read subpref coordinates
subpref_coord = {}
for line in f2:
subpref_id, lon, lat = line.split('\t')
lon = float(lon)
lat = float(lat)
subpref_id = int(subpref_id)
subpref_coord.keys().append(subpref_id)
subpref_coord[subpref_id] = (lon, lat)
f2.close()
# if wanna plot number of users whose this is home subpref
for subpref in range(1,256):
lons[subpref] = subpref_coord[subpref][0]
lats[subpref] = subpref_coord[subpref][1]
for u, v, d in G.edges(data=True):
lo = []
la = []
lo.append(lons[u])
lo.append(lons[v])
la.append(lats[u])
la.append(lats[v])
x, y = m(lo, la)
m.plot(x,y, color = color_val)
return plt
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
num = ["%.2f" % traj[subpref_id]]
# for name, xc, yc in zip(num, x, y):
# plt.text(xc, yc, name)
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(str(1 - traj[subpref_id]))
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
#plt.savefig('/home/sscepano/D4D res/allstuff/CLUSTERING/subpref res/maps/grayscale_pct_home_c_map2.png',dpi=1000)
plt.show()
squarecorner = squareprolist[plotsquareCount].cornerPoints
shadowlons = [
squarecorner[0].x, squarecorner[1].x, squarecorner[2].x,
squarecorner[3].x
]
shadowlats = [
squarecorner[0].y, squarecorner[1].y, squarecorner[2].y,
squarecorner[3].y
]
x, y = m(shadowlons, shadowlats)
shpsegs = []
shpsegs.append(list(zip(x, y)))
lines = LineCollection(shpsegs, antialiaseds=(1, ))
#不可航行区域
if (squareprolist[plotsquareCount].isNavigonal == False):
lines.set_facecolors(cm.jet(0.1))
lines.set_edgecolors('g')
lines.set_linewidth(0.6)
lines.set_alpha(0.6) #设置透明度
ax.add_collection(lines) #绘制不可行区域
else:
lines.set_facecolors(cm.jet(0.02))
lines.set_edgecolors('b')
lines.set_linewidth(1.2)
#lines.set_alpha(0.1) #设置透明度
# 设置颜色深度，权重越大深度越大
weight = Naviweight.get(squareprolist[plotsquareCount].offsetCoord, 1)
if weight < 1:
weight = 1
if weight > 10:
shp = ShapeFile('.../states/statessp020')
dbf = dbflib.open('../states/statessp020')
for npoly in range(shp.info()[0]):
# 在地图上绘制彩色多边形
shpsegs = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for rings in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x, y))
if ring == 0:
shapedict = dbf.read_record(npoly)
name = shapedict['STATE']
lines = LineCollection(shpsegs, antialiaseds=(1, ))
# state_to_code字典，例如'ALASKA' -> 'AK'， omitted
try:
per = obama[state_to_code[name.upper()]]
except KeyError:
continue
lines.set_facecolors('k')
lines.set_alpha(0.75 * per)
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
plt.show()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
# if max(lons) > 721. or min(lons) < -721. or max(lats) > 91. or min(lats) < -91:
# raise ValueError,msg
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(col[subpref_id])
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.savefig('/home/sscepano/D4D res/allstuff/simple dynamics/v1/cleaned_mod_v1.png',dpi=1000)
#plt.show()
###################################################################################################3
def map_fq(fq_scaled):
print fq_scaled
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib as mpl
mpl.rcParams['font.size'] = 10.
mpl.rcParams['axes.labelsize'] = 8.
mpl.rcParams['xtick.labelsize'] = 6.
mpl.rcParams['ytick.labelsize'] = 6.
fig = plt.figure(1)
#Custom adjust of the subplots
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
m.drawcoastlines()
from shapelib import ShapeFile
import dbflib
from matplotlib.collections import LineCollection
from matplotlib import cm
shp = ShapeFile(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
msg = "Out of bounds"
color_col = []
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
#num = ["%.2f" % data[subpref_id]]
# for name, xc, yc in zip(num, x, y):
# plt.text(xc, yc, name)
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
#print float("%.7f" % fq_scaled[subpref_id])
lines.set_facecolors(str(1.0 - fq_scaled[str(subpref_id)]))
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.savefig('/home/sscepano/D4D res/allstuff/CLUSTERING/subpref no home res/scaled_subpref_Calling_fq2.png',dpi=1000)
#plt.show()
return
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
#color_col
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
if subpref_cluster3[subpref_id] == 1:
lines.set_facecolors('r')
if subpref_cluster3[subpref_id] == 2:
lines.set_facecolors('b')
if subpref_cluster3[subpref_id] == 3:
lines.set_facecolors('g')
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.savefig('/home/sscepano/D4D res/allstuff/CLUSTERING/res/maps/pca/kmeans10args_3clusters.png',dpi=1000)
#plt.show()
###################################################################################################3
subpref_cluster2 = rd.read_in_subpref_assigned_2clusters()
def map_communities_and_commutes(G):
G_mod = nx.read_gml("/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/total_commuting_G_scaled_weights_11COM_713_7115.gml")
col = [str]*256
for i in range(256):
col[i] = 'w'
for node in G_mod.nodes_iter(data=True):
#print node[1]['label']
col_gephi = node[1]['graphics']['fill']
while (len(col_gephi) < 7):
col_gephi += '0'
subpref_gephi = int(float(node[1]['label']))
print subpref_gephi, col_gephi
col[subpref_gephi] = col_gephi
#print col
plt.clf()
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
# read the shapefile archive
s = m.readshapefile('/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE', 'subpref')
from shapelib import ShapeFile
import dbflib
from matplotlib.collections import LineCollection
from matplotlib import cm
shp = ShapeFile(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(col[subpref_id])
lines.set_edgecolors('gray')
lines.set_linewidth(0.3)
ax.add_collection(lines)
m.drawcoastlines()
plt.show()
# # data to plot on the map
# lons = [int]*256
# lats = [int]*256
#
# # read in coordinates fo subprefs
# file_name2 = "/home/sscepano/DATA SET7S/D4D/SUBPREF_POS_LONLAT.TSV"
# f2 = open(file_name2, 'r')
#
# # read subpref coordinates
# subpref_coord = {}
# for line in f2:
# subpref_id, lon, lat = line.split('\t')
# lon = float(lon)
# lat = float(lat)
# subpref_id = int(subpref_id)
# subpref_coord.keys().append(subpref_id)
# subpref_coord[subpref_id] = (lon, lat)
#
# f2.close()
#
# # if wanna plot number of users whose this is home subpref
# for subpref in range(1,256):
# lons[subpref] = subpref_coord[subpref][0]
# lats[subpref] = subpref_coord[subpref][1]
#
#
# if G.has_node(-1):
# G.remove_node(-1)
#
# max7s = 1
# min7s = 1
# for u,v,d in G.edges(data=True):
# if d['weight'] > max7s:
# max7s = d['weight']
# if d['weight'] < min7s:
# min7s = d['weight']
#
# max7s = float(max7s)
# print max7s
# print min7s
#
# scaled_weight = defaultdict(int)
# for i in range(256):
# scaled_weight[i] = defaultdict(int)
#
# for u,v,d in G.edges(data=True):
# node1 = G.nodes(data=True)[u][1]['label']
# node2 = G.nodes(data=True)[v][1]['label']
# print u, node1
# print v, node2
# print d
# scaled_weight[node1][node2] = (d['weight'] - min7s) / (max7s - min7s)
#
## for u,v,d in G.edges(data=True):
## print u,v,d
## node1 = G.nodes(data=True)[u][1]['label']
## node2 = G.nodes(data=True)[v][1]['label']
## print node1, G_mod.nodes(data=True)[u][1]['label']
## print node2, G_mod.nodes(data=True)[v][1]['label']
#
#
# for u, v, d in G.edges(data=True):
# node1 = G.nodes(data=True)[u][1]['label']
# node2 = G.nodes(data=True)[v][1]['label']
# print node1
# print node2
# lo = []
# la = []
# print u
# print v
# lo.append(lons[node1])
# lo.append(lons[node2])
# la.append(lats[node1])
# la.append(lats[node2])
# #m.drawgreatcircle(lons[u],lats[u], lons[v],lats[v])
# x, y = m(lo, la)
# #linewidth7s = d['weight']
# #linewidth7s = d['weight'] / max7s
# #lons, lats = n.meshgrid(lo,la)
# linewidth7s = scaled_weight[node1][node2] * 7 + 0.2
# m.plot(x,y, 'b', linewidth = linewidth7s)
# #wave = 0.75*(np.sin(2.*lats)**8*np.cos(4.*lons))
# #mean = 0.5*np.cos(2.*lats)*((np.sin(2.*lats))**2 + 2.)
# #m.contour(x,y,linewidth=linewidth7s)
# #m.quiver(x,y,lons, lats, latlon=True)
## if linewidth7s > 1:
## print linewidth7s
#
#
# figure_name = "/home/sscepano/D4D res/allstuff/User movements graphs/commuting patterns/1/maps/mod_classes_SCALED_11COM_713_7115.png"
# print(figure_name)
# plt.savefig(figure_name, format = "png",dpi=1000)
return
# print(record)
# faire plus beau
if record[6] == "Alpes-de-Haute-Provence":
name = "Alpes-Hte-Provence"
elif record[6] == "Territoire de Belfort":
name = "Terr. de Belfort"
elif record[6] == "Seine-Saint-Denis":
name = "Seine-St-Denis"
elif record[6] == "Paris":
name = "Paris (Ville)"
else:
name = record[6]
print("rec : ", name)
# erreurs pour (dans rec):
# - Alpes-de-Haute-Provence -> Alpes-Hte-Provence
# - Territoire de Belfort -> Terr. de Belfort
# - Seine-Saint-Denis -> Seine-St-Denis
# - Paris -> Paris (Ville)
dep = df.loc[df['DEPARTEMENT'] == name]
print(dep['DEP/TOT'])
dens = 0
colorVal = scalarMap.to_rgba(dep['DEP/TOT'])
lines.set_facecolors(colorVal)
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
plt.savefig('france2.png', dpi=300)
plt.show()
dbf = dbflib.open('../states/statesp020')
for npoly in range(shp.info()[0]):
# draw colored polygons on the map
shpseqs = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons,lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
name = shapedict['STATE']
lines = LineCollection(shpsegs,antialiased=(1,))
# state_to_code dict, e.g. 'ALASKA' -> 'AK', omitted
try:
per = obama[state_to_code[name.upper()]]
except KeyError:
continue
lines.set_facecolors('k')
lines.set_alpha(0.75 * per) # shrink the percentage a bit
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.show()
def map_diversity():
#subpref_avg_fq = rd.read_in_subpref_avg_fq()
rg = get_scaled_rg()
fig = plt.figure(1)
#Custom adjust of the subplots
plt.subplots_adjust(left=0.05,right=0.95,top=0.90,bottom=0.05,wspace=0.15,hspace=0.05)
ax = plt.subplot(111)
m = Basemap(llcrnrlon=-9, \
llcrnrlat=3.8, \
urcrnrlon=-1.5, \
urcrnrlat = 11, \
resolution = 'h', \
projection = 'tmerc', \
lat_0 = 7.38, \
lon_0 = -5.30)
m.drawcoastlines()
from shapelib import ShapeFile
import dbflib
from matplotlib.collections import LineCollection
from matplotlib import cm
shp = ShapeFile(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
dbf = dbflib.open(r'/home/sscepano/DATA SET7S/D4D/SubPrefecture/GEOM_SOUS_PREFECTURE')
msg = "Out of bounds"
color_col = []
for npoly in range(shp.info()[0]):
shpsegs = []
shpinfo = []
shp_object = shp.read_object(npoly)
verts = shp_object.vertices()
rings = len(verts)
for ring in range(rings):
lons, lats = zip(*verts[ring])
x, y = m(lons, lats)
shpsegs.append(zip(x,y))
if ring == 0:
shapedict = dbf.read_record(npoly)
#print shapedict
name = shapedict["ID_DEPART"]
subpref_id = shapedict["ID_SP"]
num = ["%.2f" % rg[subpref_id]]
# add information about ring number to dictionary.
shapedict['RINGNUM'] = ring+1
shapedict['SHAPENUM'] = npoly+1
shpinfo.append(shapedict)
#print subpref_id
#print name
lines = LineCollection(shpsegs,antialiaseds=(1,))
lines.set_facecolors(str(rg[subpref_id]))
lines.set_edgecolors('k')
lines.set_linewidth(0.3)
ax.add_collection(lines)
plt.savefig('/home/sscepano/D4D res/allstuff/diversity of travel/diversity_map.png',dpi=500)
return
segs = [data,] else: segs = [] for i in range(1,len(shape.parts)): index = shape.parts[i-1] index2 = shape.parts[i] segs.append(data[index:index2]) segs.append(data[index2:]) lines = LineCollection(segs,antialiaseds=(1,)) #Now obtain the data in a given poly and assign a color to the value div = str(record[5]) #print record dval = divlookup(dfile,div,yyyy,int(mm)) #if(dval > valmax): dval = valmax - 0.1 lines.set_facecolors(cmap_temp([dval/cwidth])) lines.set_edgecolors(cmap_temp([dval/cwidth])) lines.set_linewidth(0.25) ax1.add_collection(lines) if(imgsize == 'GEO'): inProj = Proj(init='epsg:3338') outProj = Proj(init='epsg:4326') #Now read in the Alaska Climate Division Shapes and fill the basemap s = shapefile.Reader(r"./Shapefiles/AK_divisions_NAD83") shapes = s.shapes() records = s.records() for record, shape in zip(records,shapes): lons,lats = zip(*shape.points)
lons,lats = zip(*shape.points)
data = np.array(m(lons, lats)).T
if len(shape.parts) == 1:
segs = [data,]
else:
segs = []
for i in range(1,len(shape.parts)):
index = shape.parts[i-1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs,antialiaseds=(1,))
lines.set_facecolors(np.random.rand(3, 1) * 0.5 + 0.5)
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
cnt += 1
infile = open(curdir +'state_info_revised.csv','r')
csvfile = csv.reader(infile)
for lakepoly in m.lakepolygons:
lp = Polygon(lakepoly.boundary, zorder=3)
lp.set_facecolor(thisblue)
lp.set_linewidth(0.1)
## within this loop, we will also check that the shape is not in multiple parts, and if yes, segment the points in different ensembles:
if sovereignt == 'Uganda':
color = '#2b8cbe'
else:
color = '#a6bddb'
continue
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
lines.set_facecolors(color)
lines.set_edgecolors('k')
lines.set_linewidth(0.1)
ax.add_collection(lines)
m.drawcoastlines(linewidth=0.5)
plt.savefig('map.png', dpi=600)
plt.close()
def choropleth(self,
adm_num_dicts,
level='department',
source='gadm',
cmap_base=plt.cm.YlOrRd,
ret_colormap_and_label=True,
bin_lims=None,
nbins=5,
linewidth=0.4):
"""
A choropleth map by department or municipality. The dataset for boundaries of administrative areas is taken from GADM database
Original code is from here: https://github.com/astivim/Nicaragua-Population-Density-Map
Input:
- adm_num_dicts: a list of dictionaries with the following key-value pairs: {'adm' : str, 'num' : float}
- bin_lims (optional): values of bin edges
- nbins (optional): number of bins, default = 5. Ignored if bin_lims is specified.
- cmap_base: base colormap. Default is plt.cm.YlOrRd (red)
- ret_colormap_and_label=True. If True, return colormap_label = {'bin_labels' : bin_labels, 'colormap' : custom_cmap} used for plotting. If False, colormap_label = None
Return: colormap_label = {'bin_labels' : bin_labels, 'colormap' : custom_cmap}
"""
# Divide nums into bins. For each entry in nums, assign to which bin it belongs to, then assign a color to each bin.
adms = [item['adm'] for item in adm_num_dicts]
nums = [item['num'] for item in adm_num_dicts]
# Define bin_lims by dividing the range of nums into equally spaced portions
if bin_lims:
nbins = len(bin_lims)
else:
temp_nums = [num for num in nums if num]
bin_spacing = (max(temp_nums) - min(temp_nums) + 1) / nbins
bin_lims = np.arange(min(temp_nums),
max(temp_nums) + bin_spacing, bin_spacing)
nbins = len(bin_lims)
# Assign each num in nums into its respective bin and assign color. Use only nbins values from the colormap (cmap)
nums_bin_ix = np.digitize(nums, bin_lims)
nums_bin_ix = [idx if idx < nbins else 0 for idx in nums_bin_ix]
cmaplist = [cmap_base(i) for i in range(cmap_base.N)]
del_colors = np.int(np.ceil(cmap_base.N / float(nbins - 1)))
map_colors = cmaplist[0::del_colors]
map_colors.insert(
0,
(1.0, 1.0, 1.0, 1.0)) # fill with white if no data (num is None)
# Load the geographical data (shapefile and record)
if level == 'department':
fpath = os.path.join(self.data_dir, "NIC_adm/NIC_adm1")
elif level == 'municipality':
fpath = os.path.join(self.data_dir, "NIC_adm/NIC_adm2")
else:
print("Level unrecognized. Use 'department'")
fpath = os.path.join(self.data_dir, "NIC_adm/NIC_adm1")
r = shapefile.Reader(fpath)
shapes = r.shapes()
records = r.records()
# Extract lat and lon data from the GADM shapefile and assign a facecolor to each department
for record, shape in zip(records, shapes):
lons, lats = zip(*shape.points)
data = [(lon, lat) for lon, lat in zip(*self(lons, lats))]
if level == 'department':
adm_name = record[4]
else:
adm_name = record[6]
try:
adm_idx = adms.index(adm_name)
adm_num = adm_num_dicts[adm_idx]['num']
color_idx = nums_bin_ix[adm_idx]
except ValueError:
adm_num = None
color_idx = 0
if len(shape.parts) == 1:
segs = [
data,
]
else:
segs = []
for i in range(1, len(shape.parts)):
index = shape.parts[i - 1]
index2 = shape.parts[i]
segs.append(data[index:index2])
segs.append(data[index2:])
lines = LineCollection(segs, antialiaseds=(1, ))
color = map_colors[color_idx] if adm_name not in [
"Lago Nicaragua", "Lago de Nicaragua"
] else 'aqua'
lines.set_facecolors(color)
lines.set_linewidth(linewidth)
self.ax.add_collection(lines)
if ret_colormap_and_label:
custom_cmap = mpl.colors.ListedColormap(map_colors[1:],
name='from_list')
bins = [(i1, i2)
for i1, i2 in zip(bin_lims[0:nbins], bin_lims[1:nbins + 1])
]
bin_labels = ["(%d - %d)" % (b[0], b[1]) for b in bins]
colormap_label = {
'bin_labels': bin_labels,
'colormap': custom_cmap
}
else:
colormap_label = None
return colormap_label
